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sklearn.neural_network.multilayer_perceptron.MLPClassifier

Visibility: public Uploaded 13-08-2021 by Sergey Redyuk
sklearn==0.18
numpy>=1.6.1
scipy>=0.9 0 runs

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activation | default: "relu" | |

alpha | L2 penalty (regularization term) parameter | default: 0.0001 |

batch_size | Size of minibatches for stochastic optimizers If the solver is 'lbfgs', the classifier will not use minibatch When set to "auto", `batch_size=min(200, n_samples)` learning_rate : {'constant', 'invscaling', 'adaptive'}, default 'constant' Learning rate schedule for weight updates - 'constant' is a constant learning rate given by 'learning_rate_init' - 'invscaling' gradually decreases the learning rate ``learning_rate_`` at each time step 't' using an inverse scaling exponent of 'power_t' effective_learning_rate = learning_rate_init / pow(t, power_t) - 'adaptive' keeps the learning rate constant to 'learning_rate_init' as long as training loss keeps decreasing Each time two consecutive epochs fail to decrease training loss by at least tol, or fail to increase validation score by at least tol if 'early_stopping' is on, the current learning rate is divided by 5 Only used when ``solver='sgd'`` | default: "auto" |

beta_1 | Exponential decay rate for estimates of first moment vector in adam, should be in [0, 1). Only used when solver='adam' | default: 0.9 |

beta_2 | Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1). Only used when solver='adam' | default: 0.999 |

early_stopping | Whether to use early stopping to terminate training when validation score is not improving. If set to true, it will automatically set aside 10% of training data as validation and terminate training when validation score is not improving by at least tol for two consecutive epochs Only effective when solver='sgd' or 'adam' | default: false |

epsilon | Value for numerical stability in adam. Only used when solver='adam' | default: 1e-08 |

hidden_layer_sizes | The ith element represents the number of neurons in the ith hidden layer activation : {'identity', 'logistic', 'tanh', 'relu'}, default 'relu' Activation function for the hidden layer - 'identity', no-op activation, useful to implement linear bottleneck, returns f(x) = x - 'logistic', the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)) - 'tanh', the hyperbolic tan function, returns f(x) = tanh(x) - 'relu', the rectified linear unit function, returns f(x) = max(0, x) solver : {'lbfgs', 'sgd', 'adam'}, default 'adam' The solver for weight optimization - 'lbfgs' is an optimizer in the family of quasi-Newton methods - 'sgd' refers to stochastic gradient descent - 'adam' refers to a stochastic gradient-based optimizer proposed by Kingma, Diederik, and Jimmy Ba Note: The default solver 'adam' works pretty well on relatively large datasets (with thousands of training samples or more) in terms of both training t... | default: [100] |

learning_rate | default: "constant" | |

learning_rate_init | The initial learning rate used. It controls the step-size in updating the weights. Only used when solver='sgd' or 'adam' | default: 0.001 |

max_iter | Maximum number of iterations. The solver iterates until convergence (determined by 'tol') or this number of iterations | default: 200 |

momentum | Momentum for gradient descent update. Should be between 0 and 1. Only used when solver='sgd' | default: 0.9 |

nesterovs_momentum | Whether to use Nesterov's momentum. Only used when solver='sgd' and momentum > 0 | default: true |

power_t | The exponent for inverse scaling learning rate It is used in updating effective learning rate when the learning_rate is set to 'invscaling'. Only used when solver='sgd' | default: 0.5 |

random_state | State or seed for random number generator | default: null |

shuffle | Whether to shuffle samples in each iteration. Only used when solver='sgd' or 'adam' | default: true |

solver | default: "adam" | |

tol | Tolerance for the optimization. When the loss or score is not improving by at least tol for two consecutive iterations, unless `learning_rate` is set to 'adaptive', convergence is considered to be reached and training stops | default: 0.0001 |

validation_fraction | The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1 Only used if early_stopping is True | default: 0.1 |

verbose | Whether to print progress messages to stdout | default: false |

warm_start | When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution | default: false |

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